Broadband wireless systems are currently in a rapid evolutionary phase in terms of development of various technologies, development of various applications, deployment of various services and generation of many important standards in the field. The increasing demand on various services justifies the need for the transmission of data on various communication channels at the highest possible data rates. The multipath and fading characteristics of the wireless channels result in various distortions, the most important of those being the inter-symbol interference (ISI) especially at relatively high data rates. Adaptive equalizers are employed to mitigate the ISI introduced by the time varying dispersive channels and possibly arising from other sources. In one class of adaptive equalizers, a training sequence known to the receiver is transmitted that is used by adaptive equalizer for adjusting the equalizer parameter vector to a value that results in a relatively small residual ISI. After the training sequence, the data is transmitted during which period the equalizer continues to adapt to slow channel variations using decision directed method.
Among the various algorithms to adapt the equalizer parameter vector are the recursive least squares (RLS) algorithm, weighted Kaman filter, LMS algorithm, and the quantized state (QS) algorithm, the last one taught by Kumar et. al. in “Adaptive Equalization Via Fast Quantized-State Methods,” IEEE Transactions on Communications, Vol. COM-29, No. 10, October 1981. Kumar at. al. teach orthogonalization process to arrive at fast and computationally efficient identification algorithms in, “State Inverse and Decorrelated State Stochastic Approximation,” Automatica, Vol. 16, May 1980. The training approach, however, is not desirable in many communication applications such as those involving video conference type of applications that will require a training sequence every time a different speaker talks. Moreover, the need for training sequence results in a significant reduction in capacity as for example, in GSM standard, a very significant part of each frame is used for the equalizer training sequence. Also, if during the decision-directed mode the equalizer deviates significantly due to burst of noise or interference, all the subsequent data will be erroneously received by the receiver until the loss of equalization is detected and the training sequence is retransmitted and so on.
There are many other applications where the equalizers are applied as in antenna beam forming, adaptive antenna focusing of the antenna, radio astronomy, navigation, etc. For example, Kumar et. al. teach in Method and Apparatus for Reducing Multipath Signal Error Using Deconvolution, U.S. Pat. No. 5,918,161, June 1999, an equalizer approach for a very different problem of precise elimination of the multipath error in the range measurement in GPS receiver. In all of the various applications of equalizers and due to various considerations such as the logistics and efficiency of systems, it has been of great interest to have the equalizer adapt without the need for a training sequence. Such equalizers are the termed the “blind mode” equalizers.
Among some of the approaches to blind mode equalization are the Sato's algorithm and Goddard's algorithm that are similar to the LMS and RLS algorithms, respectively, except that these may not have any training period. Kumar, in “Convergence of A Decision-Directed Adaptive Equalizer,” Proceedings of the 22nd IEEE Conference on Decision and Control, 1983, Vol. 22, teaches a technique wherein an intentional noise with relatively high variance is injected into the decision-directed adaptive algorithm with the noise variance reduced as the convergence progressed and shows that the domain of convergence of the blind mode equalizer was considerably increased with the increase in the noise variance at the start of the algorithm. The technique taught by Kumar is analogous to the annealing in the steel process industry and in fact the term simulated annealing was coined after the introduction by Kumar. Lambert et. al., teach the estimation of the channel impulse response from the detected data in, “Forward/Inverse Blind Equalization,” 1995 Conference Record of the 28th Asilomar Conference on Signals, Systems and Computers, Vol. 2, 1995. Another blind mode equalization method applicable to the case where the modulated data symbols have a constant envelope and known as constant modulus algorithm (CMA) taught by Goddard in “Self-recovering Equalization and Carrier Tracking in Two-Dimensional Data Communication System,” IEEE Transactions on Communications, vol. 28, No. 11, pp. 1867-1875, November, 1980, is based on minimization of the difference between the magnitude square of the estimate of the estimate of the data symbol and a constant that may be selected to be 1.
The prior blind mode equalizers have a relatively long convergence period and are not universally applicable in terms of the channels to be equalized and in some cases methods such as the one based on polyspectra analysis are computationally very expensive. The CMA method is limited to only constant envelope modulation schemes such as M-phase shift keying (MPSK) and thus are not applicable to modulation schemes such as M-quadrature amplitude modulation (MQAM) and M-amplitude shift keying (MASK) modulation that are extensively used in wireless communication systems due to their desirable characteristics. Tsuie et. al. in, Selective Slicing Equalizer, Pub. No. US 2008/0260017 A1, Oct. 23, 2008, taught a selective slicing equalizer wherein in a decision feedback equalizer configuration, the input to the feedback path may be selected either from the combiner output or the output of the slicer depending upon the combiner output.
The prior blind mode equalization techniques may involve local minima to which the algorithm may converge resulting in high residual ISI. Thus it is desirable to have blind mode adaptive equalizers that are robust and not converging to any local minima, have wide applicability without, for example, restriction of constant modulus signals, are relatively fast in convergence, and are computationally efficient. The equalizers of this invention possess these and various other benefits.
Various embodiments described herein are directed to methods and systems for blind mode adaptive equalizer system to recover the in general complex valued data symbols from a signal transmitted over time-varying dispersive wireless channels. For example, various embodiments may utilize an architecture comprised of a channel gain normalizer comprised of a channel signal power estimator, a channel gain estimator and a parameter α estimator for providing nearly constant average power output and for adjusting the dominant tap of the normalized channel to close to 1, a blind mode equalizer with hierarchical structure (BMAEHS) comprised of a level 1 adaptive system and a level 2 adaptive system for the equalization of the normalized channel output, and an initial data recovery for recovery of the data symbols received during the initial convergence period of the BMAEHS and pre appending the recovered symbols to the output of the BMAEHS providing a continuous stream of all the equalized symbols.
The level 1 adaptive system of the BMAEHS is further comprised of an equalizer filter providing the linear estimate of the data symbol, the decision device providing the detected data symbol, an adaptation block generating the equalizer parameter vector on the basis of a first correction signal generated within the adaptation block and a second correction signal inputted from the level 2 adaptive system. The cascade of the equalizer filter and the decision device is referred to as the equalizer. The first correction vector is based on the error between the input and output of the decision device. However, in the blind mode of adaptation, the first error may converge to a relatively small value resulting in a false convergence or convergence to one of the local minima that are implicitly present due to the nature of the manner of generation of the first error. To eliminate this possibility, the level 2 adaptive system estimates a modeling error incurred by the equalizer. The modeling error is generated by first obtaining an independent estimate of the channel impulse response based on the output of the decision device and the channel output and determining the modeling error as the deviation of the impulse response of the composite system comprised of the equalizer filter and the estimate of the channel impulse response from the ideal impulse comprised of all but one of its elements equal to 0. In case the equalizer tends to converge to a false minimum, the magnitude of the modeling error gets large and the level 2 adaptive system generates the second correction signal to keep the modeling error small and thereby avoiding convergence to a false or local minimum.
In the invented architecture, the channel gain estimator is to normalize the output of the channel so as to match the level of the normalized signal with the levels of the slicers present in the decision device, thereby resulting in increased convergence rate during the initial convergence phase of the algorithm. This is particularly important when the modulated signal contains at least part of the information encoded in the amplitude of the signal as, for example, is the case with the MQAM and MASK modulation schemes.
In another one of the various architectures of the invention for blind mode adaptive equalizer system, the BMAEHS is additionally comprised of an orthogonalizer, wherein the two correction signals generated in level 1 and level 2 adaptive systems are first normalized to have an equal mean squared norms and wherein the orthogonalizer provides a composite orthogonalized correction signal vector to the equalizer. The process of orthogonalization results in introducing certain independence among the sequence of correction signal vectors. The orthogonalization may result in fast convergence speeds in blind mode similar to those in the equalizers with training sequence.
In another one of the various architectures of the invention for blind mode adaptive equalizer system, the BMAEHS is replaced by a cascade of multiple equalizer stages with multiplicity m greater than 1 and with each equalizer stage selected to be one of the BMAEHS or the simpler blind mode adaptive equalizer (BMAE). In the architecture, the input to the ith equalizer stage is the linear estimate of data symbol generated by the (i−1)th equalizer stage, and the detected data symbol from the (i−1)th equalizer stage provides the training sequence to the ith equalizer stage during the initial convergence period of the ith equalizer stage for i=2, . . . , m. In one of the embodiments of the architecture, m=2, with the first equalizer stage selected to be a BMAEHS and the second equalizer stage selected to be a BMAE. In the cascaded architecture, the BMAEHS ensures convergence bringing the residual ISI to a relatively small error such that the next equalizer stage may employ a relatively simple LMS algorithm, for example. The equivalent length of the cascade equalizer is the sum of the lengths of the m stages, and the mean squared error in the estimation of the data symbol depends upon the total length of the equalizer, the cascaded architecture has the advantage or reduced computational requirements without a significant loss in convergence speed, as the computational requirement may vary more than linearly with the length of the equalizer,
Various architectures of the invention use a linear equalizer or a decision feedback equalizer in the level 1 adaptive system. In one of the various architectures of the invention, FFT implementation is used for the generation of the second correction signal resulting in a further significant reduction in the computational requirements.
In various embodiments of the invention, an adaptive communication receiver for the demodulation and detection of digitally modulated signals received over wireless communication channels exhibiting multipath and fading is described with the receiver comprised of an RF front end, an RF to complex baseband converter, a band limiting matched filter, a channel gain normalizer, a blind mode adaptive equalizer with hierarchical structure, an initial data segment recovery circuit, a differential decoder, a complex baseband to data bit mapper, and an error correction code decoder and de-interleaver providing the information data at the output of the receiver without the requirements of any training sequence.
The differential decoder in the adaptive receiver performs the function that is inverse to that of the encoder in the transmitter. The differential encoder is for providing protection against phase ambiguity with the number of phase ambiguities equal to the order of rotational symmetry of the signal constellation of the baseband symbols. The phase ambiguities may be introduced due to the blind mode of the equalization. The number of phase ambiguities for the MQAM signal is 4 for any M equal to N2 with N equal to any integer power of 2, for example M=16 or 64. The architecture for the differential encoder is comprised of a phase threshold device for providing the reference phase for the sector to which the baseband symbol belongs, a differential phase encoder, an adder to modify the output of the differential phase encoder by a difference phase, a complex exponential function block, and a multiplier to modulate the amplitude of the baseband symbol onto the output of the complex exponential function block, and applies to various modulation schemes. The architecture presented for the decoder is similar to that of the differential encoder and is for performing the function that is inverse to that of the encoder.
In various embodiments of the invention, an adaptive beam former system is described with the system comprised of an antenna array, a bank of RF front ends receiving signals from the antenna array elements and a bank of RF to baseband converters with their outputs inputted to the adaptive digital beam former that is further comprised of an adaptive combiner, a combiner gain normalizer, a decision device, and a multilevel adaptation block for receiving data symbols transmitted form a source in a blind mode without the need for any training sequence and without the restriction of a constant modulus on the transmitted data.